
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "31"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 81 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 77 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.125301	0.512063	12.512940	20.390688	85.341196	7.571417	5.726962	23.738007	0.7700	0.7819	0.2612	12.768793	14.553842
2459593	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.577585	3.782718	3.593798	22.641580	1.334576	4.242796	1.100602	4.286566	0.7282	0.7239	0.2554	5.379532	5.071505
2459592	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.146087	0.032990	0.134460	2.292111	0.483089	1.159868	1.798115	0.131529	0.0291	0.0282	0.0014	nan	nan
2459591	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.417416	4.157375	5.132258	26.722531	1.544852	2.326148	-0.073078	0.524699	0.6757	0.6782	0.3822	4.638731	4.344133
2459590	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.940377	4.550570	4.510404	25.390985	1.333494	3.622814	0.607552	0.938173	0.6754	0.6762	0.3746	5.217628	5.033200
2459589	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.195650	3.986421	21.487037	23.140308	114.940797	6.071882	5.296646	10.271833	0.6906	0.6983	0.3042	4.227955	4.571537
2459588	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	204.543603	221.844921	inf	inf	21.844476	26.763086	48.300189	18.456416	nan	nan	nan	0.000000	0.000000
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	350.212506	350.526898	inf	inf	-1.599316	107.877693	-4.675000	576.856526	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.051502	2.813342	-0.527570	2.779111	-0.573237	0.390708	-0.425829	0.670983	0.6747	0.6880	0.3683	4.311191	4.434912
2459585	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.836773	6.549988	0.213207	3.543554	-0.006756	3.243152	2.446573	1.886019	0.0323	0.0309	0.0016	0.000000	0.000000
2459584	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	19.918985	21.728357	1.961911	8.246997	3.452150	28.785664	-0.260214	0.096855	0.6291	0.6368	0.3751	5.051477	5.059036
2459583	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.603906	3.483888	0.142990	8.179372	0.410346	1.626398	0.065193	1.610830	0.6710	0.6689	0.3994	4.636265	4.587780
2459582	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.723549	3.244749	-0.046963	7.619764	-0.182591	2.551475	-0.563227	0.511971	0.6743	0.6711	0.3952	4.462340	4.356287
2459581	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459580	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.714297	3.328820	-0.161733	8.614472	0.035234	1.784715	-0.802120	0.223857	0.6823	0.6826	0.3891	4.782523	4.722637
2459579	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.892426	3.448697	0.194804	10.334067	0.225498	2.258050	-0.354853	1.209589	0.6802	0.6806	0.3908	3.575815	3.568844
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.720163	-0.312927	-0.780632	-0.007127	1.194237	1.973540	-0.202125	0.776780	0.0288	0.0283	0.0016	nan	nan
2459577	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.617665	4.760077	0.156801	8.036822	0.290931	1.324084	-0.590315	0.563970	0.6817	0.6850	0.3904	4.014312	3.820468
2459576	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.721625	3.673958	0.143419	6.812248	2.623757	2.471975	0.041687	0.742588	0.6851	0.6908	0.3877	3.368173	3.221603
2459575	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.166949	2.744217	1.590475	6.090944	29.259053	4.842262	0.341548	8.198513	0.7436	0.7490	0.3309	0.000000	0.000000
2459574	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.640950	3.347180	1.268113	6.270017	50.504300	1.324757	0.383019	0.156628	0.6914	0.6943	0.3775	4.085719	3.859391
2459573	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.117311	3.358505	0.087054	10.503308	0.923439	2.254100	-0.477395	0.919986	0.6769	0.6768	0.4008	0.000000	0.000000
2459572	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.796839	3.356385	0.008786	6.244364	0.161743	0.721709	0.023842	1.784957	0.6779	0.6795	0.4014	4.400977	3.927090
2459571	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.674325	-0.443553	-0.770426	0.033231	40.841209	0.534261	-0.002967	0.944748	0.0295	0.0290	0.0019	nan	nan
2459570	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.683922	8.310902	13.751022	15.667700	9.199384	8.116888	5.244763	4.926036	0.6658	0.6855	0.3777	5.010320	4.867269
2459569	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.901585	3.101972	1.301835	7.248639	15.149721	8.607837	2.625050	9.332045	0.7464	0.7503	0.2436	5.719252	5.161261
2459566	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.514864	3.397497	0.303004	7.973695	0.202181	1.919897	-0.273665	0.170075	0.6750	0.6780	0.4077	1.314582	1.348173
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	0.000000	0.000000	-0.016407	-0.016407	-0.758305	-0.758305	-0.875717	-0.875717	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.413055	0.588788	0.955434	-3.456559	-0.867136	-1.993704	-1.613893	-0.643032	0.6964	0.7090	0.3941	3.136233	3.159498
2459563	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459562	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.374689	0.062043	-0.001409	-2.169778	-1.415338	-2.482261	-1.264010	-0.673637	0.6843	0.6973	0.3946	3.146411	3.128033
2459561	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.105518	0.812059	0.546678	-1.504785	-0.909047	-2.203787	-1.557531	0.185344	0.6843	0.6973	0.3912	3.581081	3.364030
2459560	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.463502	0.402427	0.140907	-1.767663	-0.759391	-1.454331	-1.378718	-0.555444	0.6801	0.6921	0.4047	3.578144	3.484671
2459559	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.235887	0.737124	0.697402	-1.427289	-0.471570	-1.119070	-1.326291	-0.366214	0.6899	0.7056	0.3894	3.008652	3.029835
2459558	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.334487	3.994937	10.762680	5.903464	10.431655	6.938914	-1.709855	-0.602616	0.6915	0.7022	0.3982	3.042970	3.199716
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0334	0.0319	0.0013	nan	nan
2459556	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.755276	4.162377	11.248812	5.809165	9.211820	6.779098	-2.907079	-1.261929	0.6982	0.7078	0.3878	3.134825	3.182945
2459553	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6934	0.7042	0.3980	nan	nan
2459552	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.210176	4.721454	8.701932	8.349992	59.764651	7.345043	0.766245	-0.062534	0.7048	0.7061	0.3970	10.025349	3.675410
2459551	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.717794	0.420313	0.402137	-2.064568	-1.531244	-1.893574	-1.181610	1.459028	0.6875	0.6989	0.3992	3.160740	3.115732
2459550	dish_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0495	0.0490	0.0011	nan	nan
2459549	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459542	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.725508	4.127470	21.384818	12.730591	0.156567	-0.161483	-1.690706	-1.061207	0.7512	0.7518	0.3538	3.407353	3.421004
2459541	dish_ok	100.00%	1.34%	1.79%	0.00%	100.00%	0.00%	5.762284	3.709426	12.180507	6.724242	9.392519	4.943396	4.208851	2.401252	0.7416	0.7352	0.3345	4.426647	4.515812
2459540	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.051199	1.704628	1.394268	-0.358003	15.482827	-1.006840	-0.329130	-0.607677	0.7474	0.7386	0.3521	3.638270	3.414383
2459536	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.832461	5.668744	15.073041	9.577607	39.176060	6.474895	-0.806443	-1.670972	0.7295	0.7367	0.4127	4.003767	3.672787
2459535	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.517668	1.027728	1.236705	-0.409194	-2.756252	-1.204369	-1.899349	-0.709586	0.7805	0.7585	0.3692	3.514272	3.202283
2459534	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.727716	0.755135	0.291308	-1.169099	-2.003113	-1.389861	-1.163854	-0.463849	0.7733	0.7593	0.3584	7.748765	8.945512
2459533	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.710287	4.432175	10.658327	6.544767	7.824717	5.983712	-1.334814	0.891424	0.7195	0.7195	0.4009	3.274130	3.200200
2459532	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.994169	5.073217	14.018894	8.099155	10.193133	6.277756	-1.429723	-0.779147	0.7172	0.7163	0.4002	3.469036	3.413206
2459530	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.087931	0.689082	-0.849121	-1.431117	9.939243	-2.034394	-0.062004	-0.330358	0.7428	0.7347	0.3990	3.605674	3.448113
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	4.111496	2.033080	5.768992	2.628524	5.861460	8.710368	1.040738	1.745385	0.0361	0.0345	0.0017	nan	nan
2459508	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.474857	4.552722	9.519882	5.455772	9.403598	3.310882	8.925392	6.809183	0.8869	0.9197	0.2482	0.000000	0.000000
2459505	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.101022	3.972035	12.118379	5.266131	2.971075	5.697965	-0.764251	0.380447	0.8811	0.8504	0.1998	11.586378	14.309377

Antenna Season Report Notebook¶

Antenna 31 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 31, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 31: 2459604

Antenna 31: 2459603

Antenna 31: 2459602

Antenna 31: 2459598

Antenna 31: 2459597

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Antenna 31: 2459583

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Antenna 31: 2459580

Antenna 31: 2459579

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Antenna 31: 2459577

Antenna 31: 2459576

Antenna 31: 2459575

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Antenna 31: 2459573

Antenna 31: 2459572

Antenna 31: 2459571

Antenna 31: 2459570

Antenna 31: 2459569

Antenna 31: 2459566

Antenna 31: 2459565

Antenna 31: 2459564

Antenna 31: 2459563

Antenna 31: 2459562

Antenna 31: 2459561

Antenna 31: 2459560

Antenna 31: 2459559

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Antenna 31: 2459556

Antenna 31: 2459552

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Antenna 31: 2459542

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Antenna 31: 2459536

Antenna 31: 2459535

Antenna 31: 2459534

Antenna 31: 2459533

Antenna 31: 2459532

Antenna 31: 2459530

Antenna 31: 2459527

Antenna 31: 2459524

Antenna 31: 2459523

Antenna 31: 2459522

Antenna 31: 2459513

Antenna 31: 2459508

Antenna 31: 2459506

Antenna 31: 2459505

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶